Net Analyte Signal Calculation in Multivariate Calibration

Lorber, Avraham; Faber, Klaas; Kowalski, Bruce R.

doi:10.1021/ac960862b

Cited by 378 publications

(318 citation statements)

References 14 publications

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“…For first-order multivariate 906 calibration, this definition has been generalized employing an intuitive analogy between 907 instrumental signal and a so-called net analyte signal (NAS) [86]. The NAS is defined 908 as the portion of the overall signal which can be uniquely ascribed to the analyte [86,87] 909 and is completely general and applicable to all first-order calibration methods, including 910 the popular partial least-squares (PLS) algorithm [87]. Mathematically, the NAS for a 911 given analyte is defined as the projection of the sample signal orthogonal to the space 912 spanned by the interferent agents (Fig.…”

Section: Multi-way Analytical Figures Of Merit 888mentioning

confidence: 99%

Second- and higher-order data generation and calibration: A tutorial

Escandar

Goicoechea

Peña

et al. 2014

Analytica Chimica Acta

158

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Section: Multi-way Analytical Figures Of Merit 888mentioning

confidence: 99%

Second- and higher-order data generation and calibration: A tutorial

Escandar

Goicoechea

Peña

et al. 2014

Analytica Chimica Acta

158

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“…For our purposes, we chose to follow the mathematical procedure developed by Hemmateenejad and Yousefinejad, called SANAS, due to its simplicity and accuracy (68). The net analyte signal (NAS) is defined as the portion of the total signal that is directly related to the concentration of the analyte (69). For some total signal elicited from a mixture, some portion of the signal is due to the component of interest and the rest is due to the background and other components of the mixture.…”

Section: Quantitation Of Trilinolein In Triglyceride Mixtures Using Smentioning

confidence: 99%

Differential sensing for the regio- and stereoselective identification and quantitation of glycerides

Diehl

Ivy

Rabidoux

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Glycerides are of interest to the areas of food science and medicine because they are the main component of fat. From a chemical sensing perspective, glycerides are challenging analytes because they are structurally similar to one another and lack diversity in terms of functional groups. Furthermore, because animal and plant fat consists of a number of stereo-and regioisomeric acylglycerols, their components remain challenging analytes for chromatographic and mass spectrometric determination, particularly the quantitation of species in mixtures. In this study, we demonstrated the use of an array of cross-reactive serum albumins and fluorescent indicators with chemometric analysis to differentiate a panel of mono-, di-, and triglycerides. Due to the difficulties in identifying the regio-and stereochemistry of the unsaturated glycerides, a sample pretreatment consisting of olefin cross-metathesis with an allyl fluorescein species was used before array analysis. Using this simple assay, we successfully discriminated 20 glycerides via principal component analysis and linear discriminant analysis (PCA and LDA, respectively), including stereo-and regioisomeric pairs. The resulting chemometric patterns were used as a training space for which the structural characteristics of unknown glycerides were identified. In addition, by using our array to perform a standard addition analysis on a mixture of triglycerides and using a method introduced herein, we demonstrated the ability to quantitate glyceride components in a mixture.array sensing | glyceride | chemometrics | serum albumin | differential sensing

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“…Desenvolvido inicialmente para métodos de calibração direta, 28 ele só foi utilizado efetivamente quando expandido para métodos de calibração inversa (PLS e PCR) 29 e melhorado. 30 Quando todos os interferentes podem ser modelados, o NAS pode ser estimado através de amostras do branco.…”

Section: Sinal Analítico Líquido (Nas)unclassified

“…30 Quando todos os interferentes podem ser modelados, o NAS pode ser estimado através de amostras do branco. 29 Porém, neste trabalho isto não é possível, pois não é viável a produção de um queijo sem umidade. Nestes casos, o vetor NAS, , pode ser estimado para cada amostra i através do vetor de regressão de um modelo PLS com A variáveis latentes, b, de acordo com a Equação 2.…”

Section: Sinal Analítico Líquido (Nas)unclassified

Implementação de um método robusto para o controle fiscal de umidade em queijo minas artesanal. Abordagem metrológica multivariada

2013

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Recebido em 7/2/13; aceito em 29/4/13; publicado na web em 1/7/13 IMPLEMENTATION OF A ROBUST METHOD FOR QUALITY INSPECTION CONTROL OF ARTISANAL MINAS CHEESE. A MULTIVARIATE METROLOGICAL APPROACH. This study developed and validated a method for moisture determination in artisanal Minas cheese, using near-infrared spectroscopy and partial-least-squares. The model robustness was assured by broad sample diversity, real conditions of routine analysis, variable selection, outlier detection and analytical validation. The model was built from 28.5-55.5% w/w, with a root-mean-square-error-of-prediction of 1.6%. After its adoption, the method stability was confirmed over a period of two years through the development of a control chart. Besides this specific method, the present study sought to provide an example multivariate metrological methodology with potential for application in several areas, including new aspects, such as more stringent evaluation of the linearity of multivariate methods.

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Net Analyte Signal Calculation in Multivariate Calibration

Cited by 378 publications

References 14 publications

Second- and higher-order data generation and calibration: A tutorial

Second- and higher-order data generation and calibration: A tutorial

Differential sensing for the regio- and stereoselective identification and quantitation of glycerides

Implementação de um método robusto para o controle fiscal de umidade em queijo minas artesanal. Abordagem metrológica multivariada

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